Wavelet Spectral Time-Frequency Training of Deep Convolutional Neural Networks for Accurate Identification of Micro-Scale Sharp Wave Biomarkers in the Post-Hypoxic-Ischemic EEG of Preterm Sheep

ResearchSpace Repository

Show simple item record

dc.contributor.author Abbasi, Seyed en
dc.contributor.author Gunn, Alistair en
dc.contributor.author Unsworth, CP en
dc.contributor.author Bennet, Laura en
dc.coverage.spatial Montreal, Canada en
dc.date.accessioned 2020-06-11T02:51:42Z en
dc.date.available 2020-04-11 en
dc.date.issued 2020-07-20 en
dc.identifier.citation Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 2020-July: 1039-1042. 01 Jul 2020 en
dc.identifier.uri http://hdl.handle.net/2292/51474 en
dc.description.abstract Neonatal hypoxic-ischemic encephalopathy (HIE) evolves over different phases of time during recovery. Some neuroprotection treatments are only effective for specific, short windows of time during this evolution of injury. Clinically, we often do not know when an insult may have started, and thus which phase of injury the brain may be experiencing. To improve diagnosis, prognosis and treatment efficacy, we need to establish biomarkers which denote phases of injury. Our pre-clinical research, using preterm fetal sheep, show that micro-scale EEG patterns (e.g. spikes and sharp waves), superimposed on suppressed EEG background, primarily occur during the early recovery from an HI insult (0-6 h), and that numbers of events within the first 2 h are strongly predictive of neural survival. Thus, real-time automated algorithms that could reliably identify EEG patterns in this phase will help clinicians to determine the phases of injury, to help guide treatment options. We have previously developed successful automated machine learning approaches for accurate identification and quantification of HI micro-scale EEG patterns in preterm fetal sheep post-HI. This paper introduces, for the first time, a novel online fusion strategy that employs a high-level wavelet-Fourier (WF) spectral feature extraction method in conjunction with a deep convolutional neural network (CNN) classifier for accurate identification of micro-scale preterm fetal sheep post-HI sharp waves in 1024Hz EEG recordings, along with 256Hz down-sampled data. The classifier was trained and tested over 4120 EEG segments within the first 2 hours latent phase recordings. The WF-CNN classifier can robustly identify sharp waves with considerable high-performance of 99.86% in 1024Hz and 99.5% in 256Hz data. The method is an alternative deep-structure approach with competitive high-accuracy compared to our computationally-intensive WS-CNN sharp wave classifier. Clinical relevance—The suggested classifier could robustly identify EEG patterns of a similar morphology in preterm newborns during recovery from an HI insult. en
dc.description.uri https://embc.embs.org/2020/ en
dc.publisher IEEE en
dc.relation.ispartof 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'20) en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.rights.uri https://www.ieee.org/publications/rights/author-posting-policy.html en
dc.subject Convolutional Neural Network en
dc.subject Deep learning en
dc.subject Deep Neural Network en
dc.subject Accurate Identification en
dc.subject Seizure detection en
dc.subject EEG biomarker en
dc.subject Neonatal seizure en
dc.subject Hypoxic-Ischemic en
dc.subject Spectral en
dc.subject Time-Frequency en
dc.subject sharp wave en
dc.subject Preterm brain injury en
dc.subject machine learning en
dc.subject wavelet-Fourier en
dc.subject high-performance en
dc.subject early diagnosis en
dc.subject CNN classifier en
dc.subject Clinical recording en
dc.subject signal processing en
dc.subject pattern recognition en
dc.subject neuroscience en
dc.subject sheep en
dc.title Wavelet Spectral Time-Frequency Training of Deep Convolutional Neural Networks for Accurate Identification of Micro-Scale Sharp Wave Biomarkers in the Post-Hypoxic-Ischemic EEG of Preterm Sheep en
dc.type Conference Item en
dc.identifier.doi 10.1109/EMBC44109.2020.9176057 en
dc.rights.holder Copyright: IEEE en
pubs.finish-date 2020-07-24 en
pubs.publication-status Accepted en
pubs.start-date 2020-07-20 en
dc.rights.accessrights http://purl.org/eprint/accessRights/OpenAccess en
pubs.subtype Conference Paper en
pubs.elements-id 800478 en
pubs.org-id Bioengineering Institute en
pubs.org-id Medical and Health Sciences en
pubs.org-id Medical Sciences en
pubs.org-id Physiology Division en
pubs.record-created-at-source-date 2020-04-29 en


Files in this item

Find Full text

This item appears in the following Collection(s)

Show simple item record

Share

Search ResearchSpace


Advanced Search

Browse

Statistics